A comparison of the performance of the 3-D super-ensemble and an ensemble Kalman filter for short-range regional ocean prediction

Abstract

This study compares the ability of two approaches integrating models and data to forecast the Ligurian Sea regional oceanographic conditions in the short-term range (0–72 hours) when constrained by a common observation dataset. The post-processing 3-D super-ensemble (3DSE) algorithm, which uses observations to optimally combine multi-model forecasts into a single prediction of the oceanic variable, is first considered. The 3DSE predictive skills are compared to those of the Regional Ocean Modeling System model in which observations are assimilated through a more conventional ensemble Kalman filter (EnKF) approach. Assimilated measurements include sea surface temperature maps, and temperature and salinity subsurface observations from a fleet of five underwater gliders. Retrospective analyses are carried out to produce daily predictions during the 11-d period of the REP10 sea trial experiment. The forecast skill evaluation based on a distributed multi-sensor validation dataset indicates an overall superior performance of the EnKF, both at the surface and at depth. While the 3DSE and EnKF perform comparably well in the area spanned by the incorporated measurements, the 3DSE accuracy is found to rapidly decrease outside this area. In particular, the univariate formulation of the method combined with the absence of regular surface salinity measurements produces large errors in the 3DSE salinity forecast. On the contrary, the EnKF leads to more homogeneous forecast errors over the modelling domain for both temperature and salinity. The EnKF is found to consistently improve the predictions with respect to the control solution without assimilation and to be positively skilled when compared to the climatological estimate. For typical regional oceanographic applications with scarce subsurface observations, the lack of physical spatial and multivariate error covariances applicable to the individual model weights in the 3DSE formulation constitutes a major limitation for the performance of this multi-model-data fusion approach compared to conventional advanced data assimilation strategies.

• operational oceanography
• regional ocean prediction
• data assimilation
• ensemble Kalman filter
• 3-D super-ensemble
• underwater gliders
• Ligurian Sea

Acknowledgements

We thank the Italian Air Force National Meteorological Center (Centro Nazionale per la Meteorologia e Climatologia Aeronautica, CNMCA) for providing COSMO-ME data, EU FP7 EUROSITES for providing the observations at ODAS ITALIA 1 mooring, Alexander Barth (University of Liège) for sharing the ROMS Ligurian Sea model grid and the Istituto Nazionale di Geofisica e Vulcanologia Bologna for delivering the MFS predictions. We also thank the institutions providing the ocean forecasts used as input for the 3DSE: IFREMER-SHOM-Meteo France for the PREVIMER system and the Naval Research Laboratory–Stennis Space Center for the NCOM model. We are grateful to Chuck Trees for the scientific lead of the REP10 experiment, and Richard Stoner, Daniele Cecchi, Giuliana Pennucci, Gisella Baldasserini, Craig Lewis, Matt Cofin and Domencio Galletti for maintaining and piloting the gliders. The North Atlantic Treaty Organization funded this work.

Notes

Computed as the square root of the average mean square difference.